ASTM E1790-00
(Practice)Standard Practice for Near Infrared Qualitative Analysis
Standard Practice for Near Infrared Qualitative Analysis
SCOPE
1.1 This practice covers the use of near-infrared (NIR) spectroscopy for the qualitative analysis of liquids and solids. The practice is written under the assumption that most NIR qualitative analyses will be performed with instruments designed specifically for this region and equipped with computerized data handling algorithms. In principle, however, the practice also applies to work with liquid samples using instruments designed for operation over the ultraviolet (UV), visible, and mid-infrared (IR) regions if suitable data handling capabilities are available. Many Fourier Transform Infrared (FTIR) (normally considered mid-IR instruments) have NIR capability, or at least extended-range beamsplitters that allow operation to 1.2 [mu]m; this practice also applies to data from these instruments.
1.2 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health practices and determine the applicability of regulatory limitations prior to use.
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Designation:E1790–00
Standard Practice for
Near Infrared Qualitative Analysis
This standard is issued under the fixed designation E 1790; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (e) indicates an editorial change since the last revision or reapproval.
1. Scope 3.2.1.1 Discussion—This differs from diffuse reflectance,
where the returning radiation exits the same portion of the
1.1 This practice covers the use of near-infrared (NIR)
surface of the material as the illuminating radiation entered.
spectroscopy for the qualitative analysis of liquids and solids.
3.2.2 training sample (otherwise called a “reference
The practice is written under the assumption that most NIR
sample” or “standard”), n—a quantity of material of known
qualitative analyses will be performed with instruments de-
composition or properties, or both, presented to an instrument
signed specifically for this region and equipped with comput-
for measurement in order to find relationships between the
erized data handling algorithms. In principle, however, the
measurements and the composition or properties, or both, of
practice also applies to work with liquid samples using
the sample.
instruments designed for operation over the ultraviolet (UV),
3.2.2.1 Discussion—This term is typically used in conjunc-
visible, and mid-infrared (IR) regions if suitable data handling
tion with computerized methods for ascertaining the relation-
capabilities are available. Many Fourier Transform Infrared
ships.
(FTIR) (normally considered mid-IR instruments) have NIR
capability, or at least extended-range beamsplitters that allow
Training samples for quantitative analysis (also called “calibration
operation to 1.2 µm; this practice also applies to data from samples,” as in Practices E 1655) have different requirements than
training samples used for qualitative analysis.
these instruments.
1.2 This standard does not purport to address all of the
4. Significance and Use
safety concerns, if any, associated with its use. It is the
4.1 NIR spectroscopy is a widely used technique for quan-
responsibility of the user of this standard to establish appro-
titative analysis, and it is also becoming more widely used for
priate safety and health practices and determine the applica-
the identification of organic materials, that is, qualitative
bility of regulatory limitations prior to use.
analysis. In general, however, the concept of qualitative analy-
2. Referenced Documents
sis as used in the NIR spectral region differs from that used in
the mid-IR spectral region in that NIR qualitative analysis
2.1 ASTM Standards:
refers to the process of automated comparison of the spectra of
E 131 Terminology Relating to Molecular Spectroscopy
unknown materials to the spectra of known materials in order
E 1252 Practice for General Techniques for Obtaining In-
to identify the unknown. This approach constitutes a library
frared Spectra for Qualitative Infrared Analysis
search method in which each user generates his own library.
E 1655 Practices for Infrared, Multivariate, Quantitative
4.2 Historically, NIR spectroscopy as practiced with classi-
Analysis
cal UV-VIS-NIR instruments using methods similar to those
3. Terminology
described in Practice E 1252 was not considered to be a strong
technique for qualitative analysis. Although the positions and
3.1 Definitions—For definitions of general terms and sym-
intensities of absorption bands in specific wavelength ranges
bols pertaining to NIR spectroscopy and statistical computa-
were used to confirm the presence of certain functional groups,
tions, refer to Terminology E 131.
the spectra were not considered to be specific enough to allow
3.2 Definitions of Terms Specific to This Standard:
unequivocal identification of unknown materials.Afew impor-
3.2.1 interactance, n—the phenomenon whereby radiant
tant libraries of NIR spectra were developed for qualitative
energy entering the surface of a material is scattered by the
purposes, but the lack of suitable data handling facilities
material back to the surface, but at a different portion of the
limited the scope of qualitative analysis severely. Furthermore,
surface.
earlier work was limited almost entirely to liquid samples.
4.3 Currently, the mid-IR procedure of deducing the struc-
This practice is under the jurisdiction ofASTM Committee E-13 on Molecular
ture of an unknown material via analysis of the locations,
Spectroscopy and is the direct responsibility of Subcommittee E13.11 on Chemo-
strengths, and positional shifts of individual absorption bands
metrics.
is generally not used in the NIR.
Current edition approved Sept. 10, 2000. Published November 2000. Originally
published as E 1790 – 96. Last previous edition E 1790 – 96.
Annual Book of ASTM Standards, Vol 03.06.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.
E1790
4.4 With the development of specialized NIR instruments chance of false identification, although care must be taken that
and mathematical algorithms for treating the data, it became an unknown material not in the library is not identified as a
possible to obtain a wealth of information from NIR spectra
similar material that is in the library.
that had hitherto gone unused. While the mathematical algo-
5.1.3 Measurements may be made via transmission, reflec-
rithms described in this practice can be applied to spectral data
tion, or any other optical setup suitable for collecting NIR
in any region, this practice describes their application to the
spectra. In practice, only transmission and diffuse reflection
NIR.
have been in common use.
4.5 The application of NIR spectroscopy to qualitative
5.1.4 Determination of the relationships between absor-
analysis in the manner described is relatively new, and proce-
bances at different wavelengths for a set of materials and
dures for this application are still evolving. The application of
consolidation of these relationships into a set of criteria for
chemometric methods to spectroscopy has limitations, and the
identifying those materials requires the use of computerized
limitations are not all defined yet since the techniques are
learning algorithms. These algorithms can also take into
relatively new. One area of concern to some scientists is the
account extraneous variations such as are found, for example,
effect of low-level contaminants. Any analytical methodology
when measurements are made on powdered solids.
has its detection limits, and NIR is no different in this regard,
5.1.5 Instrumentation is commercially available for making
but neither would we expect it to be any worse. Since the
suitable measurements in the NIR spectral region. Manufac-
relatively broad character of NIR bands makes it unlikely that
turer’s instructions should be followed to ensure correct
a contaminant would not overlap any of the measured wave-
operation,optimumaccuracy,andsafetybeforecollectingdata.
lengths, the question would only be one of degree: whether a
given amount of contaminant could be detected. The user must 5.1.6 NIR spectroscopy has, as one of its paradigms, that
be aware of the probable contaminants he is liable to run into little or no sample preparation be required. In conformance
and account for the possibility of this occurring, perhaps by
with that paradigm, sample preparation steps in other spectro-
includingdeliberatelycontaminatedsamplesinthetrainingset. scopic technologies are replaced with sample presentation
methodologies in NIR analysis. The most common sample
5. General
presentation methods are the following:
5.1 NIR qualitative analysis is conducted by comparison of
5.1.6.1 DiffuseReflectance—Solidmaterialsaregroundinto
NIR absorption spectra of unknown materials with those of
powder (or used as-is, if already in suitably fine powder form)
knownreferencematerials.Sincetheabsorptionbandsofmany
and packed into a cup, which allows the surface of the sample
substances of interest are less distinctive in the NIR than in the
to be illuminated and the reflected radiant power measured.
mid-IR spectral region, the analytical capability of the tech-
5.1.6.2 “Transflectance”—Clear or scattering liquids are
nique relies heavily on the accuracy of the absorption mea-
placed in a cup containing a transparent window with a
surements and the relationship of the relative absorbances at
diffusely reflecting material behind the sample. Any radiant
different wavelengths. Materials to be identified are measured
energy passing through the sample is reflected diffusely by the
by a NIR spectrometer, and the spectral data thus generated are
backingmaterial,sothenetmeasurementisjustlikethediffuse
saved in an auxiliary computer attached to the spectrometer
reflectance measurement of powdered solids.
proper. One of the several algorithms described in Section 6 is
5.1.6.3 Transmission—Liquids or solids are placed in cells
then applied to the data in order to generate classification
criteria, which can then be applied to data from unknown with two transparent windows and measured by transmission.
samples in order to classify (or identify) them as being the
5.1.6.4 FiberProbes—Illuminating and collecting fibers are
same as one of the previously seen materials. Good chemical
brought in parallel to the sample. A variety of optical configu-
laboratory practice should be followed to help ensure repro-
rations are used to couple the radiant energy from the fibers to
ducible results for each material. The preparation and presen-
the sample and back again, in an optical “head” of some sort.
tation of samples to the instrument should be consistent within
Transmittance, reflectance, and interactance have all been used
a library, and unknowns should be treated the same way that
at the sample end of the fiber to couple the radiation to the
the training samples were.
sample.Interactancemeasurementsaresometimesmadebythe
5.1.1 The technique is applicable to liquids, solids, and
simple expedient of pressing the end of a fiber bundle
gases. For analysis of gases, multipath vapor cells capable of
containing mixed illuminating and receiving fibers against the
achieving up to 100-metre path lengths may be required.
sample surface.
Spectra of vapors and gases may be sensitive to the total
5.2 To connect the mathematics with the spectroscopy used,
sample pressure, and this has to be determined for each type of
the procedure can be generally described as follows:
sample.
(1) The spectral measurements define some multidimen-
5.1.2 Unknownsamplestobeidentifiedmaybeprescreened
sional space. The axes in that space are the absorbances at the
based on criteria other than their NIR spectra (for example,
various wavelengths, or some mathematical transformation
visual inspection). The training samples (that is, the “knowns”
thereof.
used to teach the algorithm what different materials look like)
(2) Groups of spectra for the same material define some
may also be similarly prescreened and grouped into libraries of
region in the multidimensional space.
similar materials (for example, liquids and solids). The un-
known is then compared with only those materials in the (3) The analysis involves determining which region the
appropriate library. The prescreening will help reduce the unknown falls in.
E1790
5.2.1 Problems with this type of analysis include the fol- multivariateanalysisatvaryinglevelsofmathematicalabstrac-
lowing:insufficientseparationofthegroupsinthemultidimen- tion (see, for example, Refs (1-5), a useful starting point but
sional space to allow for classification (indicating insufficient
far from exhaustive list); most of the algorithms used for NIR
differences among the spectra of the materials involved),
qualitative analysis are relatively straightforward applications
inadequate representation of measurement variability within
of these methods.
groups during training (indicating an insufficient number or
6.1.1 Implementations of these algorithms are available in
variety of training samples), or poor detection limits for minor
standard generic statistical software packages (for example,
contaminants.
SAS, BMDP, and SPSS). In addition, the manufacturers of
5.2.2 To optimize the methods against these potential prob-
modern NIR spectrometers include implementations of these
lem areas, generation of a method occurs in three stages. In the
algorithms in their proprietary software packages that run on
first, or training stage, known samples are presented to the
the auxiliary computers supplied with the spectrometers; this
instrument. The data collected are then presented to one of the
approach has the advantage that the software matches the
various algorithms and are thus used to “train” the algorithm to
format and nature of the data generated by the spectrometer. In
recognize the various different materials.
either case, the details of the algorithms and their implemen-
5.2.3 In the second, or validation stage, the ability of the
tations are usually transparent to the user.
algorithm to correctly recognize materials not in the training
6.2 Calculation of Mahalanobis distances has been de-
set of samples is tested.
scribed (5-9) in the literature directly for application to NIR
5.2.4 In the third, or use stage, unknown samples are
spectra. The Mahalanobis distance is a way of measuring
presented to the instrument, which then compares the data so
whether a given sample falls within a given region of multi-
obtained to the data from the known samples and decides
dimensional space, since a small distance indicates that the
whether the data from the unknown agrees with the data from
sample is “close to” the center of the region, and thus within it.
any of the known materials.The unknown material is classified
The training samples define a region of space so that a
as whichever material gives the closest agreement to the data.
multidimensional ellipsoid includes a specified fraction of
5.2.5 Optionally, the algorithm may provide for the case in
these samples; the distance from the center of the region to the
whichthedatafromtheunknowndoesnotagreewiththatfrom
ellipsoid surface (that is, the equivalent of a “diameter”)
anyoftheknownssufficientlywelltopermitidentification,and
defines the Mahalanobis distance. The Mahalanobis distance is
refuse to identify the unknown sample.
5.3 Samples to be identified during the use stage must be in calculated from the matrix equation:
the same phase and physical condition as the known samples
2 t
D 5 ~x 2 x¯~i!! M ~x 2 x¯~i!! (1)
i u u
were during the
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